基于半直接法的无人集群协同视觉SLAM算法

doi:10.12382/bgxb.2023.0547

摘要/Abstract

摘要：

协同定位和环境感知技术是无人集群实现自主导航的基石,但受制于大规模无人集群系统小型个体平台的计算、载荷、带宽等资源所限,诸多相关技术难以实际部署应用。为实现资源约束下大规模无人集群的精准定位与环境感知,提出一种基于半直接法的轻量化协同视觉SLAM算法,设计融合光流法和直接法的半直接特征点跟踪方法,采用集中式双向通讯策略,使得大规模无人集群系统在面对通讯干扰和延迟时拥有较高的容错率,同时兼具准确性和快速性。基于EuRoC数据集和实际物理环境对算法开展对比实验,结果表明:新算法的实时性能平均提升60%,显著优于其他基于特征法的协同视觉SLAM算法;在丢包率小于40%以及通讯延迟低于0.1s的低质量通讯环境中,新算法定位精度更高、鲁棒性更强。

关键词: 无人集群, 协同视觉SLAM, 半直接法视觉里程计, 轻量化

Abstract:

The collaborative positioning and environmental awareness technologies are the cornerstone of autonomous navigation of unmanned swarm. However, due to the limitations of computing, load, bandwidth and other resources of small-sized individual platforms in large-scale unmanned swarm systems, many related technologies are difficultly deployed and applied in practice. In order to achieve the accurate positioning and environmental awareness of large-scale unmanned swarm under resource constraints, a lightweight collaborative visual SLAM algorithm based on semi-direct method is proposed, and a semi-direct feature point tracking method that combines the optical flow method and the direct method is designed. The centralized two-way communication strategy is used to make the large-scale unmanned swarm system have a high fault-tolerant rate in the face of communication interference and delay, and the unmanned swarm system. The comparative experiments were conducted on the algorithm based on the EuRoC dataset and actual physical environment. The results show that the real-time performance of the proposed algorithm is improved by an average of 60%, which is significantly better than other feature-based collaborative visual SLAM algorithms. In a low-quality communication environment where the packet loss rate is less than 40% and the communication delay is less than 0.1 second, the proposed algorithm has higher positioning accuracy and stronger robustness.

Key words: unmanned swarm, collaborative visual SLAM, semi-direct visual odometry, lightweight

中图分类号:

曹昊哲, 刘全攀. 基于半直接法的无人集群协同视觉SLAM算法[J]. 兵工学报, 2023, 44(11): 3345-3358.

CAO Haozhe, LIU Quanpan. Unmanned Swarm Collaborative Visual SLAM Algorithm Based on Semi-direct Method[J]. Acta Armamentarii, 2023, 44(11): 3345-3358.

图/表 33

图1 关键帧和非关键帧的跟踪

Fig.1 Tracking of keyframes and non-keyframes

图2 直接图像对齐

Fig.2 Direct image alignment

图3 非关键帧与局部地图对齐

Fig.3 Alignment of non-keyframes with local map

图4 边缘化策略

Fig.4 Marginalization strategy

图5 基于半直接法的协同SLAM算法框架

Fig.5 Framework of collaborative SLAM algorithm based on semi-direct method

图6 无人平台与地面站的交互信息

Fig.6 Interactive information of unmanned platform and ground station

图7 3辆无人车同时运行时的简图

Fig.7 Schematic diagram of three unmanned vehicles running at the same time

图8 3辆无人车同时运行时的因子图表示

Fig.8 Factor graph representation of three unmanned vehicles running at the same time

图9 回环检测触发前后的轨迹和点云地图对比

Fig.9 Comparison of trajectories and point cloud maps before and after loop closure detection triggering

图10 地图匹配

Fig.10 Map matching

图11 回环检测触发前后的轨迹和点云地图对比

Fig.11 Comparison of trajectories and point cloud maps before and after loop closure detection triggering

图12 通讯控制器

Fig.12 Communication controller

图13 通讯延迟下从无人平台到地面站的通讯带宽

Fig.13 Communication bandwidth from the unmanned platforms to the ground station under communication delay

图14 通讯延迟下从地面站到无人平台的通讯带宽

Fig.14 Communication bandwidth from the ground station to the unmanned platforms under communication delay

图15 通讯干扰下从无人平台到地面站的通讯带宽

Fig.15 Communication bandwidth from the unmanned platforms to the ground station under communication interference

图16 通讯干扰下从地面站到无人平台的通讯带宽

Fig.16 Communication bandwidth from the ground station to the unmanned platforms under communication interference

图17 通讯延迟对定位精度的影响

Fig.17 Effect of communication delay on positioning accuracy

图18 通讯干扰对定位精度的影响

Fig.18 Effect of communication interference on positioning accuracy

图19 通讯延迟对定位精度的影响(通讯干扰40%)

Fig.19 Effect of communication delay on positioning accuracy (communication interference: 40%)

图20 通讯干扰对定位精度的影响(通讯延迟0.3s)

Fig.20 Effect of communication interference on positioning accuracy (communication delay: 0.3s)

图21 EuRoC数据集的点云地图

Fig.21 Point cloud maps of EuRoC dataset

图22 EuRoC数据集的轨迹图

Fig.22 Trajectories of EuRoC dataset

图23 EuRoC数据集的MH_01,MH_02和MH_03的点云地图和轨迹图

Fig.23 Point cloud maps and trajectories of EuRoC dataset

表1 定位精度对比

Table 1 Comparison of positioning accuracy m

算法		数据集
算法		MH_01	MH_02	MH_03	V2_01	V2_02
DSO		0.057	0.092	0.198	0.077	0.095
COVINS		0.041	0.060	0.056	0.042	0.053
CCM-SLAM		0.106	0.092	0.156	0.202	0.098
	Agent1	0.035			0.056
无协同	Agent2		0.032			0.087
	Agent3			0.029
协同		0.028	0.030	0.027	0.047	0.062

表2 跟踪1帧图像所花费的平均时间

Table 2 Average time spent tracking a frame of image ms

算法	数据集
算法	MH_01	MH_02	MH_03	V2_01	V2_02
CCM-SLAM	37.82	35.31	34.20	38.57	31.13
COVINS	17.67	13.60	12.28	13.83	13.04
本文算法	6.72	6.58	7.87	7.51	10.22

图24 关键帧和非关键帧数量和跟踪时间消耗的对比

Fig.24 Comparison of keyframes and non-keyframes in number and tracking time consumption

图25 物理环境实验

Fig.25 Real environment experiment

图26 地面无人平台SLAM算法测试系统

Fig.26 SLAM algorithm test system for ground unmanned platform

图27 2辆无人车在室内环境中构建的轨迹和点云地图

Fig.27 Trajectories and point cloud maps constructed by two unmanned vehicles in an indoor environment

图28 3辆无人车在室内环境中构建的轨迹和点云地图

Fig.28 Trajectories and point cloud maps constructed by three unmanned vehicles in an indoor environment

图29 4辆无人车在室内环境中构建的轨迹和点云地图

Fig.29 Trajectories and point cloud maps constructed by four unmanned vehicles in an indoor environment

图30 4辆无人车同时运行时从无人平台到地面站的通讯带宽

Fig.30 Communication bandwidth from the unmanned platforms to the ground station when four unmanned vehicles are running at the same time

图31 4辆无人车同时运行时从地面站到无人平台的通讯带宽

Fig.31 Communication bandwidth from the ground station to the unmanned platforms when four unmanned vehicles are running at the same time

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